Methods and apparatus for generating, updating and distributing speech recognition models

ABSTRACT

Techniques for generating, distributing, and using speech recognition models are described. A shared speech processing facility is used to support speech recognition for a wide variety of devices with limited capabilities including business computer systems, personal data assistants, etc., which are coupled to the speech processing facility via a communications channel, e.g., the Internet. Devices with audio capture capability record and transmit to the speech processing facility, via the Internet, digitized speech and receive speech processing services, e.g., speech recognition model generation and/or speech recognition services, in response. The Internet is used to return speech recognition models and/or information identifying recognized words or phrases. Thus, the speech processing facility can be used to provide speech recognition capabilities to devices without such capabilities and/or to augment a device&#39;s speech processing capability. Voice dialing, telephone control and/or other services are provided by the speech processing facility in response to speech recognition results.

FIELD OF THE INVENTION

The present invention is directed to speech recognition techniques and,more particularly, to methods and apparatus for generating speechrecognition models, distributing speech recognition models andperforming speech recognition operations, e.g., voice dialing and wordprocessing operations, using speech recognition models.

BACKGROUND OF THE INVENTION

Speech recognition, which includes both speaker independent speechrecognition and speaker dependent speech recognition, is used for a widevariety of applications.

Speech recognition normally involves the use of speech recognitionmodels or templates that have been trained using speech samples providedby one or more individuals. Commonly used speech recognition modelsinclude Hidden Markov Models (HMMS). An example of a common template isa dynamic time warping (DTW) template. In the context of the presentapplication “speech recognition model” is intended to encompass bothspeech recognition models as well as templates which are used for speechrecognition purposes.

As part of a speech recognition operation, speech input is normallydigitized and then processed. The processing normally involvesextracting feature information, e.g., energy and/timing information,from the digitized signal. The extracted feature information normallytakes the form of one or more feature vectors. The extracted featurevectors are then compared to one or more speech recognition models in anattempt to recognize words, phrases or sounds.

In speech recognition systems, various actions, e.g., dialing atelephone number, entering information into a form, etc., are oftenperformed in response to the results of the speech recognitionoperation.

Before speech recognition operations can be performed, one or morespeech recognition models need to be trained. Speech recognition modelscan be either speaker dependent or speaker independent. Speakerdependent (SD) speech recognition models are normally trained usingspeech from a single individual and are designed so that they shouldaccurately recognize the speech of the individual who provided thetraining speech but not necessarily other individuals. Speakerindependent (SI) speech recognition models are normally generated fromspeech provided from numerous individuals or from text. The generatedspeaker independent speech recognition models often represent compositemodels which take into consideration variations between differentspeakers, e.g., due to differing pronunciations of the same word.Speaker independent speech recognition models are designed to accuratelyidentify speech from a wide range of individuals including individualswho did not provide speech samples for training purposes.

In general, model training involves one or more individuals speaking aword or phrase, converting the speech into digital signal data, and thenprocessing the digital signal data to generate a speech recognitionmodel. Model training frequently involves an iterative process ofcomputing a speech recognition model, scoring the model, and then usingthe results of the scoring operation to further improve and retrain thespeech recognition model.

Speech recognition model training processes can be very computationallycomplex. This is true particularly in the case of SI models where audiodata from numerous speakers is normally processed to generate eachmodel. For this reason, speech recognition models are often generatedusing a relatively powerful computer systems.

Individual speech recognition models can take up a considerable amountof storage space. For this reason, it is often impractical to storespeech recognition models corresponding to large numbers of words orphrases, e.g., the names of all the people in a mid-sized company, orlarge dictionary in a portable device or speech recognizer where storagespace, e.g., memory, is limited.

In addition to limits in storage capacity, portable devices are oftenequipped with limited processing power. Speech recognition, like themodel training process, can be a relatively computationally complexprocess and can there for be time consuming given limited processingresources. Since most users of a speech processing system expect aprompt response from the system, to satisfy user demands speechprocessing often needs to be performed in real or near real time. As thenumber of potential words which may be recognized increases, so does theamount of processing required to perform a speech recognition operation.Thus, devices with limited processing power which may be able to performa speech recognition operation involving recognizing, e.g., 20 possiblenames in near real time, may not be fast enough to perform a recognitionoperation in near real time where the number of names is increased to100 possible names.

In the case of voice dialing and other applications where therecognition results need to be generated in near real time, e.g., withrelatively little delay, the limited processing power of portabledevices often limits the size of the vocabulary which can be consideredas possible recognition outcomes.

In addition to the above implementation problems, implementers of speechrecognition systems are often confronted with logistical problemsassociated with collecting speech samples to be used for model trainingpurposes. This is particularly a problem in the case of speakerindependent speech recognition models where the robustness of the modelsare often a function of the number of speech samples used for trainingand the differences between the individuals providing the samples. Inapplications where speech recognition models are to be used over a widegeographical region, it is particularly desirable that speech samples becollected from the various geographic regions where the models willultimately be used. In this manner, regional speech differences can betaken into account during model training.

Another problem confronting implementers of speech recognition systemsis that older speech recognition models may include different featureinformation than current speech recognition models. When updating asystem to use newer speech recognition models, previously used models inaddition to speech recognition software may have to be revised orreplaced. This frequently requires speech samples to retrain and/orupdate the older models. Thus the problems of collecting training dataand training speech recognition models discussed above are oftenencountered when updating existing speech recognition systems.

In systems using multiple speech recognition devices, speech modelincompatibility may require the extraction of different speech featuresfor different speech recognition devices when the devices are used toperform a speech recognition operation on the same speech segment.Accordingly, in some cases it is desirable to be able to supply thespeech to be processed to multiple systems so that each system canperform its own feature extraction operation.

In view of the above discussion, it is apparent that there is a need fornew and improved methods and apparatus relating to a wider range ofspeech recognition issues. For example, there is a need for improvementswith regard to the collecting of speech samples for purposes of trainingspeech recognition models. There is also a need for improved methods ofproviding users of portable devices with limited processing power, e.g.,notebook computers and personal data assistants (PDAs) speechrecognition functionality. Improved methods of providing speechrecognition functionality in systems where different types of speechrecognition models are used by different speech recognizers is alsodesirable. Enhanced methods and apparatus for updating speechrecognition models are also desirable.

SUMMARY OF THE INVENTION

The present invention is directed to methods and apparatus forgenerating, distributing, and using speech recognition models. Inaccordance with the present invention, a shared, e.g., centralized,speech processing facility is used to support speech recognition for awide variety of devices, e.g., notebook computers, business computersystems personal data assistants, etc. The centralized speech processingfacility of the present invention may be located at a physically remotesite, e.g., in a different room, building, or even country, than thedevices to which it provides speech processing and/or speech recognitionservices. The shared speech processing facility may be coupled tonumerous devices via the Internet and/or one or more othercommunications channels such as telephone lines, a local area network(LAN), etc.

In various embodiments, the Internet is used as the communicationschannel via which model training data is collected and/or speechrecognition input is received by the shared speech processing facilityof the present invention. Speech files may be sent to the speechprocessing facility as electronic mail (E-mail) message attachments. TheInternet is also used to return speech recognition models and/orinformation identifying recognized words or phrases included in theprocessed speech. The speech recognition models may be returned asE-mail message attachments while the recognized words may be returned astext in the body of an E-mail message or in a text file attachment to anE-mail message.

Thus, via the Internet, devices with audio capture capability andInternet access can record and transmit to the centralized speechprocessing facility of the present invention digitized speech, e.g., asspeech files. The speech processing facility then performs a modeltraining operation or speech recognition operation using the receivedspeech. A speech recognition model or data message including therecognized words, phases or other information is then returned dependingon whether a model training or recognition operation was performed, tothe device which supplied the speech.

Thus, the speech processing facility of the present invention can beused to provide speech recognition capabilities and/or to augment adevice's speech processing capability by performing speech recognitionmodel training operations and/or additional speech recognitionoperations which can be used to supplement local speech recognitionattempts.

For example, in various embodiments of the present invention, thegeneration of speech recognition models to be used locally is performedby the remote speech processing facility. In one such embodiment, whenthe local computer device needs a speech recognition model to betrained, the local computer system collects the necessary training data,e.g., speech samples from the system user and text corresponding to theretrieved speech samples and then transmits the training data, e.g., viathe Internet, to the speech processing facility of the presentinvention. The speech processing facility then generates one or morespeech recognition models and returns them to the local computer systemfor use in local speech recognition operations.

In various embodiments, the shared speech processing facility updates atraining database with the speech samples received from local computersystems. In this way, a more robust set of training data is created atthe remote speech processing facility as part of the model trainingand/or updating process without imposing addition burdens on individualdevices beyond those needed to support services being provided to a useof an individual device, e.g., notebook computer or PDA. As the trainingdatabase is augmented, speaker independent speech recognition models maybe retrained periodically using the updated training data and thentransmitted to those computer systems which use speech recognitionmodels corresponding to those models which are retrained. In thismanner, multiple local systems can benefit from one or more differentusers initiating the retraining of speech recognition models to enhancerecognition results.

As discussed above, in various embodiments, the remote speech processingfacility of the present invention is used to perform speech recognitionoperations and then return the recognition results or take other actionsbased on the recognition results. For example, in one embodimentbusiness computer systems capture speech from, e.g., customers, and thentransmit the speech or extracted speech information to the shared speechprocessing facility via the Internet. The remote speech processingfacility performs speech recognition operations on the received speechand/or received extracted speech information. The results of therecognition operation, e.g., recognized words in the form of, e.g.,text, are then returned to the business computer system which suppliedthe processed speech or speech information. The business system can thenuse the information returned by the speech processing facility, e.g.,recognized text, to fill in forms or perform other services such asautomatically respond to verbal customer inquires. Thus, the remotespeech processing method of the present invention can be used to supplyspeech processing capabilities to customers, e.g., businesses, whocan't, or do not want to, support local speech processing operations.

In addition to providing speech recognition capabilities to systemswhich can't perform speech recognition locally, the speech processingfacility of the present invention is used in various embodiments toaugment the speech recognition capabilities of various devices such asnotebook computers and personal data assistants. In such embodiments theremote speech processing facility may be used to perform speechrecognition when the local device is unable to obtain a satisfactoryrecognition result, e.g., because of a limited vocabulary or limitedprocessing capability.

In one particular exemplary embodiment, a notebook computer attempts toperform a voice dialing operation on received speech using locallystored speech recognition models prior to contracting the speechprocessing facility of the present invention. If the local speechrecognition operation fails to result in the recognition of a name, thereceived speech or extracted feature information is transmitted to theremote speech processing facility. If the local notebook computer can'tperform a dialing operation the notebook computer also transmits to theremote speech processing facility a telephone number where the user ofthe notebook computer can be contacted by telephone. The remote speechprocessing facility performs a speech recognition operation using thereceived speech and/or extracted feature information. If the speechrecognition operation results in the recognition of a name with which atelephone number is associated the telephone number is retrieved fromthe remote speech processing facility's memory. The telephone number isreturned to the device requesting that the voice dialing speechrecognition operation be performed unless a contact telephone number wasprovided with the speech and/or extracted feature information. In such acase, the speech processing facility uses telephone circuitry toinitiate one telephone call to the telephone number retrieved frommemory and another telephone call to the received contact telephonenumber. When the two calls are answered, they are bridged therebycompleting the voice dialing operation.

In addition to generating new speech recognition models to be used inspeech processing operations and providing speech recognition services,the centralized speech processing facility of the present invention canbe used for modernizing existing speech recognition system but upgradingspeech recognition models and the speech recognition engine usedtherewith. In one particular embodiment, speech recognition models ortemplates are received via the Internet from a system to be updatedalong with speech corresponding to the modeled words. The receivedmodels or templates and/or speech are used to generate updated modelswhich include different speech characteristic information or have adifferent model format than the existing speech recognition models. Theupdated models are returned to the speech recognition systems alongwith, in some cases, new speech recognition engine software.

In one particular embodiment, speech recognition templates used by voicedialing systems are updated and replaced with HMMs generated by thecentral processing system of the present invention.

At the time the templates are replaced, the speech recognition enginesoftware is also replaced with a new speech recognition engine whichuses HMMs for recognition purposes.

Various additional features and advantages of the present invention willbe apparent from the detailed description which follows.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a communication system implemented in accordance withan exemplary embodiment of the present invention.

FIG. 2 illustrates the communications system of FIG. 1 in greaterdetail.

FIG. 3 illustrates a computer system for use in the communicationssystem illustrated in FIG. 1.

FIG. 4 illustrates memory which may be used as the memory of a computerin the system illustrated in FIG. 1.

FIG. 5 illustrates a voice dialing customer record implemented inaccordance with the present invention.

FIG. 6 illustrates a voice dialing IP device which may be used in thesystem illustrated in FIG. 1.

FIG. 7 illustrates a model training routine of the present invention.

FIG. 8 illustrates an exemplary voice dialing routine of the presentinvention.

FIG. 9 illustrates a local voice dialing subroutine of the presentinvention.

FIG. 10 illustrates a remote voice dialing routine implemented inaccordance with the present invention.

FIG. 11 illustrates a call establishment routine of the presentinvention.

FIG. 12 illustrates a model generation routine of the present invention.

FIG. 13 illustrates a speech processing facility implemented inaccordance with one embodiment of the present invention.

FIG. 14 illustrates a speech recognition routine that can be executed bythe speech processing facility of FIG. 13.

DETAILED DESCRIPTION

As discussed above, the present invention is directed to methods andapparatus for generating speech recognition models, distributing speechrecognition models and performing speech recognition operations, e.g.,voice dialing and word processing operations, using speech recognitionmodels.

FIG. 1 illustrates a communications system 100 implemented in accordancewith the present invention. As illustrated, the system 100 includes abusiness premises 10 and customer premises 12, 14, 16. Each one of thepremises 10, 12, 14, 16 represents a customer or business site. Whileonly one business premise 10 is shown, it is to be understood that anynumber of business and customer premises may be included in the system100. The various premises 10, 12, 14, 16, 18 are coupled together and toa shared speech processing facility 18 of the present invention via theInternet 20 and a telephone network 22. Connections to the Internet 20may be via digital subscriber lines (DSL), cable modems, cable lines,high speed data links, e.g., T1 links, dial-up lines, wirelessconnections or a wide range of other communications channels. Thepremises 10, 12, 14, 16, 18 may be connected to the speech processingfacility via a LAN or other communications channel instead of, or inaddition to, the Internet.

While businesses have frequently contracted for high speed Internetconnections, e.g., T1 links and other high speed services, which may beon during all hours of business service, residential customers are nowalso moving to relatively high speed Internet connections which are“always on”. As part of such services, a link to the Internet ismaintained while the computer user has his/her computer on avoidingdelays associated with establishing an Internet connection when dataneeds to be sent or received over the Internet. Examples of suchInternet connections include cable modem services and DSL services. Suchservices frequently support sufficient bandwidth for the transmission ofaudio signals. As the speed of Internet connections increases, thenumber of Internet service subscribers capable of transmitting audiosignals in real or near real time will continue to increase.

The speech processing facility 18 is capable of receiving speech fromthe various premises 10, 12, 14, 16 and performing speech processingoperations thereon. The operations may include speech model training,e.g., generation, operations and/or speech recognition operations. Theresults of the speech processing operation may be returned to thecustomer or business premises from which the speech originated.Alternatively, the speech processing facility may use the results of thespeech processing operation to initiate an action such as voice dialing.In addition, received speech or data generated from received speech,such as feature vectors, may be forwarded by the speech processingfacility 18 to other devices in the system 100 for use by the receivingdevice.

The system 100 is illustrated in greater detail in FIG. 2. Inparticular, FIG. 2 provides a more detailed illustration of the firstcustomer premises 12, telephone network 22, and business premises 10.

The first customer premises 12 includes a computer system 50 and atelephone 56. The computer system 50 is coupled to the Internet 30,e.g., by a physical communications line 51 or by a wireless connectionvia antenna 52. Optionally, the computer system 50 may also be coupledto the telephone system 22 by telephone line 54, e.g., when computertelephony capabilities are supported. Telephone 56 which is also locatedat the first customer premises is coupled to the telephone system 22.Thus, a person located at the first customer premises 12 may, assumingthe computer system 50 supports telephony capability, make and/orreceive calls using either the computer 50 or telephone 56.

Business premises 10 includes a computerized business system 58 which iscoupled to the Internet 30 and a telephone system 66. Both thecomputerized business system 58 and telephone system 66 are coupled tothe telephone network 22. This allows customers to interact with thecomputer system 58 and a sales representative or operator working at thetelephone system 66. The computerized business system 58 includes aprocessor, i.e., CPU 59, memory 62, input/output device 64 and speechrecognition (SR) circuitry 60. Speech recognition circuitry 60 canperform speech recognition operations on speech obtained from a customerusing speech recognition routines and models stored in memory 62. Salesand purchasing information may be stored in memory 62 in addition to thespeech recognition routines and speech recognition models.

Telephone network 22 includes first and second telephone switches whichfunction as signal switching points (SSPs) 74, 76. The telephoneswitches 74, 76 are coupled to each other via link 80 which may be,e.g., a T1 or other high bandwidth link. The telephone network alsoincludes a voice dialing intelligent peripheral (VD IP) device 70 and aconference calling IP 78.

VD IP 70 is coupled to the Internet 30 via a network interface 72 and tothe first switch 74 via a voice and signaling connection. VD IP 70includes circuitry for performing voice dialing operations. Voicedialing operations include speech recognition operations and the placingof a call in response to the outcome of a speech recognition operation.Voice dialing IP 70 may include, for each voice dialing servicesubscriber supported by the VD IP 70, a voice dialing directory whichincludes speech recognition models of names of people who may be called,with associated telephone numbers to be dialed when the name isrecognized.

Conference calling IP 78 is coupled to both the Internet 30 and SSP 76.The connection to the SSP 76 includes both voice and signaling lines.The conference calling IP 78 can, in response to information receivedvia SSP 76 or the Internet 30, initiate calls to one or more individualsand bridge the initiated calls.

FIG. 3 illustrates the computer system 50 which may be used at one ormore customer premises, in greater detail. The computer 50 may be, e.g.,a personal computer (PC), notebook computer, or personal data assistant(PDA). As illustrated the computer 50 includes memory 302, a processor304, display device 314, input device 316, telephony circuit 308,network interface card (NIC) 318, modem 320 and audio signal processingcircuitry 322 which are coupled together via bus 313. While notillustrated in FIG. 3, in the case where wireless Internet access issupported, modem 320 may be coupled to antenna 52 shown in FIG. 2.

Processor 304, under direction of routines stored in memory 302,controls the operation of the computer 50. Information and data may bedisplayed to a user of the device 50 via display 314 while data may bemanually entered into the computer via input device, e.g., keyboard 316.The NIC 318 can be used to couple the computer 50 to a local areanetwork (LAN) or other computer network. Modem 320 may be, e.g., a DSLmodem, cable modem or other type of modem which can be used to connectthe computer system to the Internet 30. Thus, via modem 320 the computer50 can receive data from, and transmit data to, other devices coupled tothe Internet 30.

To provide the computer system 50 with the ability to perform varioustelephone functions such as dial a telephone number and host telephonecalls, the computer system 50 includes telephony circuit 308. An audioinput device, e.g., microphone 310, provides audio input to thetelephone circuit as well as audio signal processing circuitry 322. Anaudio output device, e.g., speaker 306, allows a user of the system tohear audio signals output by telephony circuit 308. Telephony circuit308 includes an option connection to telephone network 22. When theoptional connection to the telephone network 22 is not used, thetelephony circuit 308 may still receive and send audio signals via theInternet 30.

In order to support digital recording, speech recognition modeltraining, and speech recognition operations, audio signal processingcircuitry 322 is provided. Processing circuitry 322 includes a featureextractor circuit 324, a digital recording circuit 326, a speechrecognition circuit 328, and a model training circuit 330 which are allcoupled to bus 313. The feature extractor 324 and digital recordingcircuit 326 are also coupled to the audio input device for receivingthere from audio input to be processed.

Extracted feature information and digital recordings generated bycircuits 324 and 326, respectively can be stored in memory 302. Memory302 is also used to store various routines and data used by the variouscomponents of the computer system 50.

FIG. 4 illustrates exemplary contents of memory 302 in detail. Asillustrated the memory 302 includes speech recognition routines 402,speaker independent speech recognition models (SI SRMS) 404, speakerdependent speech recognition models (SD SRMS) 406, model trainingroutine 408, stored speech data 410, a voice dialing routine 416, a wordprocessor routine 418 which includes a speech recognition interface, andvoice dialing data 420.

Speech data 410 includes extracted feature information 412, e.g.,feature vectors, and digital recordings of speech 414. The featureinformation 412 and/or recordings 414 represent speech information whichcan be transmitted via the Internet for use in model training and/orspeech recognition operations. The voice dialing data 420, used duringvoice dialing operations, includes a speaker dependent voice dialingrecord 422 and a speaker independent voice dialing customer record 424.One or both of these records 422, 424 may be used to perform voicedialing operations.

Referring now to FIG. 5, there is illustrated an exemplary voice dialingcustomer record 520. As illustrated the voice dialing customer record520 includes a customer ID 501 which may be, e.g., the customer's hometelephone number. It also includes a plurality of dialing entriesrepresented by rows 510, 512, 514, 516. Each dialing entry includes atext version of a name 502 which may be spoken to initiate dialing, aspeech recognition model 504 corresponding to the name 502 in the entry,a telephone number 506 to be dialed in the event that the name isrecognized and, optionally, a speech recording 508 of the name. After aname is recognized the voice dialing routine may play the recordingassociated with the recognized name 508 back to the system user as partof a confirmation message such as “calling” followed by the playback ofthe recording. Alternatively, an audio version of the recognized namemay be generated from the text version 502 of the recognized name forconfirmation message purpose.

In addition to the name and telephone number information included in thevoice dialing customer record 520, the record also includes information520, e.g., a world wide web Internet address, identifying a remotespeech processing facility to be used in the event that a match is notidentified between the models in the record and spoken speech beingprocessed for voice dialing purposes or in the event that speechrecognition models are to be updated or generated. The memory alsoincludes a contact telephone number 522 where the user can be reachedwhen the computer system's telephone connection is not enabled.

When the voice dialing customer record 520 includes speaker dependentspeech recognition models, it may be used as the SD voice dialingcustomer record 422 shown in FIG. 4. When the voice dialing customerrecord 520 includes speaker independent speech recognition models, itmay be used as the SD voice dialing customer record 424.

Having described the computer system 50 in detail, speech processingfacility 18 will now be discussed with reference to FIG. 13. Much of thecircuitry shown in the FIG. 13 embodiment of the facility 18 is similarto that previously discussed with regard to the computer system 50.However, in the case of processors, buses, etc. the speech processingfacility 18 is generally equipped with higher capacity devices, e.g.,fast processors, a large amount of memory, high bandwidth bus, redundantLAN, telephone and Internet connections, etc.

As illustrated the speech processing system 18 includes memory 1302, aprocessor 1304, display device 1314, input device 1316, telephony/callinitiation circuit 1308, network interface card (NIC) 1318, modem 1320and audio signal processing circuitry 1322 which are coupled togethervia bus 1313. Processor 1304, under direction of routines stored inmemory 1302, controls the operation of the system 18. Information anddata may be displayed to a system administrator via display 1314 whiledata may be manually entered into the system 18 via input device 1316.The NIC 1318 can be used to couple the system to a local area network(LAN) or other computer network. Modem 1320 may be, e.g., a DSL modem,cable modem or other type of modem which can be used to connect thecomputer system to the Internet 30. Thus, via modem 1320 the system 18can receive data from, and transmit data to, other devices coupled tothe Internet 30.

To provide the system 18 with the ability to perform various telephonefunctions such as dial a telephone number and bridge telephone calls,the system 18 includes telephony/call initiation circuit 1308.

In order to support speech recognition model training, and speechrecognition operations audio signal processing circuitry 1322 isprovided. Processing circuitry 1322 includes a feature extractor circuit1324, a speech recognition circuit 1328, and a model training circuit1330 which are all coupled to bus 1313. Thus, the components of theaudio signal processing circuitry 1322 can receive audio signals andextracted speech feature information via bus 1313. Extracted featureinformation, received speech, and generated speech recognition modelscan be stored in memory 1302. Memory 1302 is also used to store variousroutines and data used by the various components of the system 18.

The contents of the memory 1302 may include voice dialing data includingvoice dialing customer records for multiple customers. The memory 1302also includes various speech recognition, call initiation and modeltraining routines. In addition, the memory 1302 includes a trainingdatabase 1209 which is a collection of speech samples used for trainingspeech recognition models, a model store 1213 for storing generatedspeech recognition models and a system/model update list which includesinformation on remote systems which are serviced by the speechprocessing system. the information includes, e.g., system identificationand contact information such as an E-mail address, the type of speechrecognition models used by the individual systems, the words in eachsystems' speech recognition vocabulary, and information on when toupdate the each systems speech recognition models.

Use of the speech processing facility to perform various operations,e.g., voice dialing, speech recognition model training and speechrecognition operations, will be discussed in detail below.

While speech processing facility 18 can support a wider range of speechprocessing operations including voice dialing, specific telephone switchperipheral devices such as VD IP 70 may be dedicated to supporting voicedialing operations. An exemplary voice dialing IP 70 which may be usedas the voice dialing IP of FIG. 2 is shown in detail in FIG. 6. The VDIP 70 can support voice dialing operations in response to speechreceived via a conventional telephone connection or via the Internet 30.Thus, the computer system 50 can use the VD IP 70 to perform a voicedialing operation. This can be done by E-mailing the VD-IP 70 a voicedialing request message including speech in an attached file.

The VD IP 70 includes a speech recognizer circuit 602, switch I/Ointerface 607, network interface 610, processor 608 and memory 612. Theprocessor 608 is responsible for controlling the overall operation ofthe voice dialing IP 70 under control of routines stored in memory 612.Memory 612 includes a speech recognition routine 613 which may be loadedinto the speech recognizer circuit 602, a voice dialing routine 614 anda call setup routine 615. The voice dialing routine 614 is responsiblefor controlling the supply of audio signals to the speech recognizercircuit 602 and controlling various operations in response torecognition results supplied by the recognizer circuit 602.

Speech recognizer 602 is coupled to a switch, e.g., SSP and receivesvoice signals therefrom. The speech recognizer circuit 602 uses speechrecognition models stored in the memory 612 and the speech recognitionroutine 613 to perform a speech recognition operation on audio signalsreceived from a telephone switch or from the Internet via networkinterface 610. Speech recognition models used by the speech recognizer602 may be speaker independent and/or speaker dependent models. Thespeech recognition models are retrieved from the personal dialer andcorporate records 618, 620 based on a customer identifier whichidentifiers the particular customer whose speech is to be processed.

The voice dialing routine 614 receives information from the speechrecognition circuit 602 which indicates the outcome of a speechrecognition operation, e.g., whether a name in the customer's record wasrecognized. If a name is recognized, and speech was received via theInternet, the telephone number corresponding to the recognized name isreturned via the Internet to the device which provided the speech.However, if a contact telephone number was received via the Internetwith the speech to be processed, the voice dialing routine 614 calls thecall setup routine 615 which is responsible for imitating a call to thetelephone number corresponding to the recognized name.

In such a case, where the customer's computer 50 will not be used toplace the call, the call setup routine 615 signals the telephone switchvia interface 606 to initiate a call to the contact telephone numberwhere the subscriber can be reached and to the telephone numbercorresponding to the recognized name. Once both parties answer, the callsetup routine instructs the switch to bridge the calls therebycompleting a call between the Internet based voice dialing service userand the party being called.

Instead of using VD IP 70, computer system 50 can use the speechprocessing facility 18 to support a voice dialing operation. Voicedialing will now be described from the perspective of computer system 50as it interacts with speech processing facility 18. FIG. 8 illustratesan exemplary voice dialing routine 416 which may be executed by thecomputer system 50.

The voice dialing routine 800 begins in start step 802 when it isexecuted, e.g., by the processor 305 of computer system 50. From step802, operation proceeds to step 804 wherein the routine monitors forspeech input. If in step 806, it is determined that speech was receivedin step 804, operation proceeds to step 808. Otherwise, operationreturns to monitoring step 804.

In step 808 a determination is made as to whether or not local speechfeature extraction is supported. If it is not, operation proceedsdirectly to step 818. However, if local feature extraction is supported,e.g., feature extractor 324 is present, operation proceeds to step 810wherein a feature extraction operation is performed on the receivedspeech. Next in step 814 a determination is made as to whether or notlocal speech recognition capability is available, e.g., a determinationis made whether or not the system 50 includes speech recognition circuit328. If in step 328 it is determined that local speech recognition isnot available, operation proceeds directly to step 818. However, iflocal speech recognition capability is available, operation proceeds tostep 812 wherein a local voice dialing sub-routine, e.g., the subroutine900 illustrated in FIG. 9 is called.

Referring now briefly to FIG. 9, it can be seen that FIG. 9 illustratesa local voice dialing subroutine 900 which can be executed by thecomputer system 50. The subroutine 900 can be used by the computersystem 50 to perform voice dialing calls without having to contact anexternal voice dialing or speech processing facility. The subroutine 900begins in start step 902, e.g., in response to being called by voicedialing routine 800. In step 902, the subroutine is provided with theextracted feature information 903 produced, e.g., in step 810, from thespeech which is to be processed for voice dialing purposes. Operationthen proceeds to step 904 wherein a speech recognition operation isperformed using the received extracted speech feature information andone or more locally stored speech recognition models, e.g., speechrecognition models obtained from the SD voice dialing customer record422 or SI voice dialing customer record 424 stored in memory 302.

In step 906 a determination is made as to whether or not a name wasrecognized as a result of the voice dialing operation. If a name was notrecognized operation proceeds to return step 908 wherein operationreturns to step 812 of the voice dialing routine 800 with an indicatorthat the local voice dialing operation was unsuccessful.

However, if a name was recognized by the speech recognition operation ofstep 904, operation proceeds from step 906 to step 910. In step 910, adetermination is made as to whether or not a computer to telephoneconnection exists. If the computer system 50 is connected to a telephoneline, operation will proceed to step 914. In step 914, the computersystem 50 is made to dial the telephone number associated, e.g., in oneof the voice dialing records 422, 424, with the recognized name. Then,in step 916, the computer system 50 detects completion of the callinitiated in step 914 before proceeding to step 918.

If in step 910 it was determined that a computer-telephone connectiondid not exist, operation proceeds to step 912. In step 912, thetelephone number to be dialed, i.e., the telephone number associatedwith the recognized name and the contact telephone number where the userof the system 50 can be reached, is transmitted, e.g., via the Internet,to a call establishment device such as conference calling IP 78. Theconference calling IP will initiate calls to both the number associatedthe recognized name and the contact number and then bridge the calls. Inthis manner, voice dialing can be used to place a call even when thecomputer system 50 is not coupled to a telephone line.

From step 912 operation proceeds to return step 918. In return step 918operation is returned to step 812 of the voice dialing routine 800 withan indicator, notifying the routine 800 that the local voice dialingoperation was successful. The indication may be, e.g., a pre-selectedvalue, message or other signal.

Upon a return from the local voice dialing sub-routine 900, operationproceeds from step 812 to step 813. In step 813 a determination is madeas to whether or not the local voice dialing operation was successful.This is determined by success/failure information returned from thesub-routine 900. If the local voice dialing operation was successful,operation proceeds to monitoring step 804 in preparation for anothervoice dialing operation. However, if the local voice dialing operationwas not successful, operation proceeds to step 818 in an attempt to useoutside resources, such as the speech process facility 18 or VD IP 70,to determine a telephone number to be dialed.

In step 818 a system user ID is transmitted to the remote speechprocessing facility 18. Then, in step 820 the received speech and/orextracted feature information is transmitted to the remote speechprocessing facility 18. Next, in step 822 a determination is made as towhether a computer to telephone line connection exits. If in step 822,it is determined that a computer-telephone connection does not exist,indicating that the system 50 cannot make a call, operation proceeds tostep 824 wherein a telephone contact number 401 is transmitted to theremote speech processing facility. The telephone contact number 401 isthe telephone number of a telephone where the user of the system 50 canbe reached.

Operation proceeds from step 824 to step 826. In the event it isdetermined in step 822 that a computer-telephone connection exists,operation will proceed directly from step 822 to step 826.

As will be discussed below, in response to the transmitted information,the speech processing facility 18 executes a voice dialing routine. Upondetecting the name of a party having an associated telephone number, theexecuted routine returns, e.g., in an E-mail message, the telephonenumber associated with the recognized name via the Internet assuming acontact telephone number was not provided to the facility 18. Thetelephone number can than be used by the computer system 50 to place acall to the party whose name was spoken. In the case where the computersystem provides a contact telephone number to the speech processingsystem 18, the system 18 realizes that the computer 50 cannot place thecall. In such a case, the remote speech processing facility 18 returns asignal indicating that the named party is being called assuming a namewas recognized or that the system was unable to identify a party to becalled in the event a name was not recognized.

In step 826, the computer system 50 detects the response sent by thespeech processing facility in response to the speech and voice dialinginformation supplied to the facility. In step 828 a determination ismade as to whether or not the received response includes a telephonenumber to be dialed. If the response does not include a telephone numberto be dialed, operation proceeds to step 829 where the system user isprovided a message indicating the results of the remote voice dialingoperation. That is, the system user is notified if the named party isbeing called or that the system was unable to identify a party to becalled. The message to be provided is indicated by the response receivedfrom the speech processing facility 18. Operation proceeds fromnotification step 829 via GOTO step 834 to monitoring step 804.

Assuming a telephone number is received from the remote speechprocessing facility, operation will proceed from step 826 to step 830wherein the computer system 50 dials the received telephone number.After call completion is detected in step 832, operation proceeds tostep 804 via GOTO step 834. In this manner, the voice dialing routinereturns to a state of monitoring for speech input, e.g., inputassociated with an attempt to place another telephone call.

Voice dialing from the perspective of the speech processing facilitywill now be described with reference to FIG. 10. A remote voice dialingroutine 1000 which may be implemented by, e.g., speech processingfacility 18, is illustrated in FIG. 10. The routine starts in step 1002when it is executed by the speech processing facility's processor. Instep 1004, voice dialing service input is received from a remote device,e.g. computer system 50, via a communications channel such as theInternet. In the case of a voice dialing operation, the input willnormally include a user ID, speech and/or extracted feature information,and optionally, a telephone contact number where the system user can bereached by telephone. This information corresponds to the informationnormally transmitted by the computer system 50 in steps 818, 822 and 824of voice dialing routine 800.

Next, in step 1006, voice dialing information is retrieved from memory.The retrieved information may include, e.g., a voice dialing recordincluding speech recognition models and corresponding telephone numbersto be used in providing voice dialing services for the identified user.The voice dialing record may be a customer specific record, e.g., partof a personal voice dialing record corresponding to the received userID, or a common voice dialing record such as a corporate voice dialingdirectory shared by many individuals including the user identified bythe received user ID.

After the dialing directory information has been retrieved, operationproceeds to step 1008 wherein a determination is made as to whether ornot extracted feature information was received. If extracted featureinformation was received operation proceeds directly to step 1012. Ifextracted feature information was not received operation proceeds tostep 1010 wherein a feature extraction operation is performed on thereceived speech. Operation proceeds from step 1010 to step 1012.

In step 1012 a speech recognition operation is performed using theretrieved voice dialing information, e.g., speech recognition models,and received or extracted feature information. The results of the speechrecognition operation are supplied to step 1014 wherein a determinationis made as to whether a name in the voice dialing directory being usedwas identified. If a name was identified operation proceeds to step1016.

In step 1016 a determination is made as to whether or not a telephonecontact number was received, e.g., in step 1004. If a telephone contactnumber was received, indicating that the user can't, or does not wantto, initiate a call from his/her computer, operation proceeds to step1018.

In step 1018 the telephone number to be dialed, i.e., the telephonenumber associated in the retrieved voice dialing information and thecontact telephone number is transmitted to a call initiation device. Theuser's ID information may also be transmitted to the call initiationdevice. The call initiation device may be, e.g., conference calling IP78 or circuitry interval to the speech processing system 18.

When the call initiation device is an external device such as conferencecalling IP 78, the telephone number to be dialed, the contact telephonenumber, and the user ID information is transmitted to the callinitiation device over any one of a plurality of communication channelsincluding the Internet, a LAN, and conventional telephone lines. Inresponse to receiving the transmitted information the call initiationdevice executes a call establishment routine, e.g., the routine 1100illustrated in FIG. 11, will initiate a call to both the telephonenumber to be dialed and the contact telephone number and then bridge thecalls when they are answered. From step 1018 of FIG. 10, operationproceeds to step 1028.

In step 1016, of FIG. 10, if it is determined that a telephone contactnumber was not received, e.g., because the device which transmitted thevoice dialing information is capable of initiating a call, operationproceeds to step 1020 wherein the telephone number to be dialed istransmitted (returned) to the remote computer system 50 in response tothe received voice dialing information, e.g., received speech and userID information. Then operation proceeds to step 1028.

Referring once again to step 1014 if it is determined in this step thata name was not recognized by the speech recognition operation thenprocessing proceeds to step 1022 instead of step 1016. In step 1022 adetermination is made as to whether there is an additional remote speechprocessing system associated with the identified user, e.g., anothersystem such as VD IP 70 which can be used support a voice dialingoperation. This determination may be made by checking information aboutthe user stored in memory.

If the answer to the inquiry made in step 1022 is no, operation proceedsto notification step 1023 prior to proceeding to STOP step 1028. In step1023 a message is sent back to the system 50 indicating to the systemthat the voice dialing attempt failed due to a failure to recognize aname.

If in step 1022 it is determined that there is an additional remotespeech processing system associated with the identified user, operationwill proceed from step 1022 to step 1024. In step 1024 the user IDinformation is transmitted to the additional remote speech processingfacility associated with the identified user. Then, in step 1026, thepreviously received speech information and/or feature information istransmitted to the additional remote speech processing facility. Thus,the additional remote speech processing facility is provided anopportunity to provide a voice dialing service when the current facilityis unable to ascertain a telephone number to be dialed. The additionalspeech processing facility, e.g., VD IP 70, will notify the user'ssystem 50 of the ultimate outcome of the voice dialing operation.

Operation proceeds from step 1026 to STOP step 1028 wherein the remotevoice dialing routine 1028 is stopped pending its execution to serviceadditional voice dialing service requests.

FIG. 11 illustrates a call establishment routine 1100 that is executedby a call initiation device, such as the conference calling IP 78 ortelephone call initiation circuit 1308, in response to call initiationinformation received as part of a voice dialing operation.

As illustrated in FIG. 11, the call establishment routine starts in step1102 when it is executed, e.g., by a processor in the conference IP 78.Then, in step 1104 a user ID, a telephone number to be dialed and acontact telephone number is received, e.g., from the speech processingfacility 18 via an Internet or telephone communications channel. Such aset of information is recognized as a request for a call initiation andbridging operation. When such information is received operation proceedsto steps 1106 and 1108. In step 1106 the conference calling IP initiatesa call using the telephone number to be dialed while in step 1108 thecontact telephone number is used to initiate a call. The initiation ofthe calls in steps 1106, 1108 may occur in parallel or serially. Oncethe two calls are answered, in step 1110, the calls are bridged. Then instep 1112 the bridged call is allowed to terminate normally, e.g., byeither of the called parties hanging up their telephone. With thetermination of the bridged call, the call establishment routine STOPS instep 1114 pending its re-execution to service additional dialingrequests from, e.g., the speech processing facility 18.

In addition to supporting voice dialing operations, the speechprocessing 18 is capable of receiving speech signals, e.g., incompressed or uncompressed digital form, generating speech recognitionmodels from the received speech, and then distributing the generatedmodels to one or more devices, e.g., voice dialing IPs, business siteswhich perform speech recognition, and individual computer systems 50. Inaccordance with one feature of the present invention speech to be usedin speech recognition model training operations, and the modelsgenerated there from, are transmitted over the Internet. Alternatively,other communications channels such as conventional telephone lines maybe used for this purpose.

Speech recognition model training will now be discussed in detail. FIG.7 illustrates a model training routine 700 which may be executed undercontrol of a user of the computer system 50 when the user desires totrain a new speech recognition model or to update an existing model,e.g., because of unsatisfactory recognition results.

The model training routine 700 begins in step 702 wherein it isinitially executed by the processor 304. Operation proceeds to step 704wherein the processor 304 receives text corresponding to the word orname to be trained or retrained. The text may be entered by a user ofthe computer system 50 via keyboard 316.

In response to receiving the text version of the word or name to betrained, e.g., modeled, in step 706 the user is prompted to state theword or name to be trained. Then, in step 708 speech received from theuser is recorded by the digital recording circuit 326. Next, in step 710a determination is made as to whether or not local feature extraction issupported. Assuming a feature extractor 324 is included in the computersystem 50, operation proceeds from step 710 to step 712. In step 712, afeature extraction operation is performed on the recorded speechresulting in the generation of a set of feature vectors corresponding tothe speech to be modeled.

Since the set of feature vectors includes speech characteristicinformation, e.g., timing, duration, amplitude and/or power informationand/or changes in these values over time, and not the actual digitizedspeech, the set of feature vectors generated in step 712 is oftenconsiderably smaller than the digital recording from which the set offeature vectors is generated.

Operation proceeds from step 712 to step 714. In cases where localfeature extraction is not supported, operation proceeds directly fromstep 710 to step 714.

In step 714 information required from the computer system 50 to train orretrain a speech recognition model and to return the resulting model, istransmitted to a speech processing facility, e.g., via the Internet. Instep 714 a user identifier is transmitted to the speech processingfacility. In addition a text version of the speech to be modeled, theextracted set of feature information corresponding to the speech to bemodeled, the digital recording of the speech to be modeled and/or analready existing speech recognition model corresponding to the speech tobe modeled, is transmitted to the speech processing facility.

As will be discussed below, the speech processing facility 18 processesthe transmitted speech or feature information by using it in a speechrecognition model training process. The speech recognition modelgenerated by the speech processing facility 18 is then returned to thecomputer system 50 for storage and/or use in speech recognitionoperations.

From step 714 operation of the computer system 50 proceeds to step 716wherein the system 50 receives, e.g., via the Internet, one or morespeech recognition models from the speech processing facility 18. Thereceived speech recognition models will include the model or modelsgenerated from the speech extracted feature information and/or otherinformation transmitted to the speech processing facility in step 714.

The received speech recognition models are stored in the computersystem's memory 302 in step 718. In the case of updated or retrainedmodels, the received model will replace the previous model or modelscorresponding to the same words, names or sounds.

As a result of storage in the memory 302, the speech recognition modelswill be available to applications which perform speech recognition suchas the voice dialing and word processor applications. After storage ofthe received models, the new model training routine 700 then stops instep 720 until being executed again to train an additional model.

In addition to providing voice dialing service, the speech processingfacility 18 can be used to provide speech recognition model trainingservices. FIG. 12 illustrates a model generation routine 1200, which canbe implemented by the speech processing facility 18. As illustrated, theroutine starts in step 1202 when it is executed by the speech processingfacility's processor. Operation then proceeds to steps 1202 and 1204which represent parallel processing paths. While the processingassociated with these paths can be performed in parallel, they can alsobe performed on a time shared basis as is commonly done in singleprocessor systems.

In step 1204 the system monitors for a model generation and/or modelupdating service request, e.g., a signal from a device such as thecomputer system 50 or computerized business system 58 indicating that aspeech recognition model needs to be generated or updated. The requestmay take the form of an E-mail message with an attachment includinginformation, speech and/or other speech data. When a request for such aservice is received, e.g. via the Internet 30, operation proceeds tostep 1206 wherein the information and data used to provide the requestedservice is received by the processor 1304, e.g., by extracting theattachment from the E-mail request message. The received informationdepends on the service to be performed.

Block 1206 a illustrates exemplary data that is received with a requestto generate a new speech recognition model. The data 1206 a includes aUser ID, speech or feature information, text information providing atext representation of the word or phrase to be modeled, and optionalspeech recognition model type information. The User Id may be atelephone number, E-mail address or some other type of uniqueidentifier. Assuming model type information is not provided a defaultmodel type will be used.

Block 1206 b illustrates exemplary data that is received with a requestto update an existing speech recognition model. The data 1206 b includesa User ID, an existing speech recognition model to be updated, existingmodel type information, speech or feature information, text informationproviding a text representation of the word or phrase to be modeled, andoptional updated speech recognition model type information. If theoptional updated speech recognition model type information is notprovided, it is assumed that the updated model is to be of the same typeas the received existing model.

Operation proceeds from step 1206 to step 1208. In step 1208, thetraining database 1209 maintained in the speech processing facility 18is augmented with the speech received in step 1206. Thus, over time, thesize and robustness of the speech training database 1211 will improvefrom the input received from various sources which use the speechprocessing facility to provide speech recognition model generation andupdating services. Since users will tend to retrain models which havebeen providing poor recognition results the quality of the training dataused for numerous subscribers is improved as each subscriber providesnew and/or additional speech samples to be used in model training.

From step 1208 operation proceeds to step 1210 wherein a speechrecognition model is generated from the received speech, featureinformation and/or other received information. Various known modeltraining techniques may be used to implement step 1210 with the trainingtechnique being used at any given time being determined by the trainingdata available and the type of speech recognition model to be generated.

In the case where speech was received, the speech normally undergoes afeature extraction operation as part of the training process. In thecase where speech feature information was received, in addition or inplace of speech, the provided feature information, e.g., featurevectors, may be used in model training thereby avoiding the need toperform a feature extraction operation on received speech.

The generated speech recognition model will be of the type specified bythe received information. In the case of a speaker dependent speechrecognition model type, the generated model will be a speaker dependentspeech recognition model. In the case of speaker independent speechrecognition model the generated model will be a speaker independentmodel. Speaker independent models are normally trained using thereceived speech and speech included in the training database 1209 astraining data. Speaker dependent models are normally generated using thereceived speech as the training data. In addition to indicating whethera generated model is to be speaker independent or speaker dependent thereceived model type information can indicate particular features orinformation which are to be used in the model, e.g., energy and deltaenergy coefficient information. In the case of models which are beingupdated, the updated model type information can specify a differentmodel type than the existing model type information.

In one particular application, a dynamic time warping (DTW) template isreceived and processed along with speech to generate a speaker dependentHidden Markov model as an updated model. In such an embodiment thereceived existing model type information would be e.g., “DTW template”and the updated model type information would be “SD HMM” indicating aspeaker dependent HMM. In this particular application, the template toHMM model conversion and training techniques discussed in U.S. Pat. No.6,014,624 which is hereby expressly incorporated by reference may beused in the model generation step 1210.

With the new or updated model generated, operation proceeds from step1210 to step 1212. In step 1212, the generated model is stored in thespeech processing facility's model store 1213. The model store includesseparate sets of models for individual users, and a common model storefor speaker independent models. The speaker independent models arestored in the corresponding user's model set which generated speakerindependent models are stored in the speaker independent model set. Themodels may be stored according to their intended application as well astype if desired. That is, models intended for voice dialing applicationsmay be stored separately in the model store 1213 from models stored forword processing operations.

From step 1212, operation proceeds to step 1214 wherein the generatedspeech recognition model is transmitted to the device from which themodel generation or updating request was received. Operation thenproceeds to step 1204 wherein the processor monitors for additionalinput, e.g., requests to generate or update additional speechrecognition models.

The processing path which begins with step 1224 executes in parallelwith the processing path which beings with step 1204. In step 1224 asystem clock is maintained. Operation proceeds from step 1224 to step1226 wherein a determination is made as to whether or not a preselectedtime corresponding to a selected time interval which is to occur betweenthe transmission of model updates has passed. If the preselected timehas not expired operation returns to step 1224. However, if thepreselected period of time has expired operation proceeds to step 1228wherein updated models stored in the model store 1213 are transmitted,e.g., via the Internet 30, to systems which use the speech recognitionmodels, e.g., systems indicated in the update list 1215 stored in thespeech processing systems memory. To avoid the needless transmission ofmodels that have not been updated only those speech recognition modelswhich have been updated, as indicated by creation time and dateinformation stored in the model store along with the models, aretransmitted to the various systems to be updated. After the updatedmodels are transmitted, operation returns to step 1224.

As an alternative to broadcasting updated speech recognition models on aperiodic basis, systems which use speech recognition models canperiodically request, from the speech processing facility 18, speechrecognition model updates via the Internet.

As discussed above, the speech processing facility 18 can be used toprovide speech recognition services in addition to voice dialing andspeech recognition model training services. Speech recognition servicecan be provided to devices, e.g., computer system 50 and businesscomputer system 58, which have speech capture capabilities but may lackspeech recognition capabilities or have relatively limited speechrecognition capabilities. Systems can transmit to the speech processingfacility 18 speech and/or extracted speech feature information, e.g.,feature vectors, and receive in response the results of a speechrecognition operation performed using the received speech or featurevectors. The speech or feature vectors may be transmitted as a fileattachment to an E-mail message sent by the system 50 or 58 over theInternet to the facility 18 requesting a speech recognition operation.The results of the speech recognition operation can be returned byE-mail to the device requesting the speech recognition operation. Theresults may be in the form of a list of words recognized in the receivedspeech or from the received feature vectors. The words may be includedin a text portion of the responsive E-mail message or in a text fileattachment.

FIG. 14 illustrates a speech recognition routine that is implemented bythe speech processing facility 18 to service speech processing requestsreceived from various devices coupled to the Internet 30. Asillustrated, the routine 1400 begins in step 1402, wherein the routine1400 is retrieved from memory 1302 and executed by the speech processingfacility's processor 1304.

Next, in step 1404, the speech processing system 18 receives a speechrecognition service request from a remote device, e.g., system 50 or 58.As mentioned above, the request may take the form of an E-mail message.The received request includes speech, e.g., compressed or uncompresseddigitized speech, and/or extracted speech feature information. This datamay be included in the form of an attached file. In addition, themessage includes a system identifier, e.g., return Email address, whichcan be used to identify the source system to which the speechrecognition results are to be returned.

From step 1404 operation proceeds to step 1406 wherein the speechprocessing facility performs a speech recognition operation using thereceived speech or received feature information in an attempt torecognize words in the received speech or speech from which the receivedfeature information was extracted. Then, in step 1408 a message isgenerated including the speech recognition results, e.g., recognizedwords, in text form. The generated message may be an E-mail message withthe source of the speech or feature information being identified as therecipient and the recognized information incorporated into the body ofthe message or an attached text file.

In step 1410 the generated message including the recognition results istransmitted, e.g., via the Internet 30, to the system which supplied thespeech or feature information used to perform the recognition operation.Then operation proceeds to step 1404 to await another request for aspeech recognition operation.

Thus, in the above described manner, through the use of the Internet andsimple E-mail messages, speech processing facility 18 provides speechrecognition services to physically remote devices which are also coupledto the Internet 30.

Numerous variations on the above described methods and apparatus arepossible without departing from the scope of the invention.

What is claimed is:
 1. A speech processing method, the method comprisingthe steps of: transmitting a stored speech recognition model including afirst set of speech information to a remote speech processing facility;transmitting to the remote speech processing facility said stored speechrecognition model being used to model a spoken word; informationidentifying a said word which is modeled by said speech recognitionmodel; receiving from the remote speech processing facility a new speechrecognition model corresponding to said word, the new speech recognitionmodel including speech characteristic information that is not includedin the stored speech recognition model.
 2. The method of claim 1,further wherein the step of transmitting the stored speech recognitionmodel includes sending the stored speech recognition model to the remoteprocessing facility via the Internet.
 3. The method of claim 2, whereinsaid new speech recognition model is a Hidden Markov Model; and whereinthe step of receiving a new speech recognition model includes: receivingthe speech recognition model via the Internet.
 4. The method of claim 3,further comprising the steps of: replacing the stored speech recognitionmodel in a memory device with the received new speech recognition model.5. The method of claim 4, wherein the stored speech recognition model isa dynamic time warping template and the new speech recognition model isa Hidden Markov Model.
 6. The method of claim 1, wherein the speechcharacteristics information that is not included in the stored speechrecognition model includes information regarding changes in signalamplitude as a function of time.
 7. A method of generating anddistributing speech recognition models, the method comprising the stepsof: receiving speech from a first speech recognition device used by afirst user; generating a first speech recognition model from the firstspeech; transmitting the first speech recognition model to the firstspeech recognition device; receiving speech from a second speechrecognition device, used by a second user the second speech recognitiondevice being different from the first speech recognition device, saidfirst user being different from said second user; generating a secondspeech recognition model from the second speech; transmitting the secondspeech recognition model to the second speech recognition device; andtransmitting the first and second speech recognition models to aplurality of additional speech recognition devices.
 8. The method ofclaim 7, wherein the first, second, and plurality of additional speechrecognition devices are coupled to the Internet; and wherein the stepsof transmitting to the second and plurality of additional speechrecognition devices includes transmitting the second speech recognitionmodel over the Internet.
 9. The method of claim 7, further comprisingthe step of: storing the first and second speech recognition models in amodel store with information indicating when the first and second speechrecognition models where created.
 10. The method of claim 9, furthercomprising the step of: determining when to transmit the first andsecond speech recognition models as a function of the storedinformation.